About this role
The Core Data Science team builds the products and data assets that let teams across Zoox measure and improve the autonomy and operational performance of our fleet. In this role, you'll focus on fleet simulation — the environment our data teams use to develop, test, and evaluate the algorithms that orchestrate our autonomous fleet (dispatch, routing, and vehicle assignment) and to understand how those algorithms perform before they reach the road. You’ll piece together the constituent components into a coherent, easy-to-use simulation environment, filling in the gaps, and building generalizable mechanisms so data scientists can measure their algorithm's impact on fleet efficiency. You'll join a diverse, experienced team with rapidly growing scope while working with one of the industry's most valuable autonomous vehicle datasets. The Core Data Science team builds the products and data assets that let teams across Zoox measure and improve the autonomy and operational performance of our fleet. In this role, you'll focus on fleet simulation — the environment our data teams use to develop, test, and evaluate the algorithms that orchestrate our autonomous fleet (dispatch, routing, and vehicle assignment) and to understand how those algorithms perform before they reach the road. You’ll piece together the constituent components into a coherent, easy-to-use simulation environment, filling in the gaps, and building generalizable mechanisms so data scientists can measure their algorithm's impact on fleet efficiency. You'll join a diverse, experienced team with rapidly growing scope while working with one of the industry's most valuable autonomous vehicle datasets. Design, prototype, and help deploy algorithms for vehicle assignment, staging, routing, and pricing Model the inputs to orchestration (rider demand, data-collection value, servicing needs, events, and policies) as objectives and constraints Work with specialists in marketplace economics, forecasting, and autonomy to turn their models into decisions the fleet acts on Measure impact on revenue, utilization, and rider experience through simulation, offline evaluation, and online experiments Contribute to how we evaluate and roll out orchestration improvements PhD or MS with 2+ years of relevant industry or research experience, in operations research, robotics, controls, transportation, computer science, applied math, or a related field Deep expertise in at least one of: combinatorial/integer optimization, stochastic optimization or control, reinforcement learning/approximate DP, network flows, or marketplace/demand modeling, plus the curiosity to work across the rest Strong coding skills in Python, C++, or Rust; you write clean, tested, version-controlled code Ability to turn ambiguous real-world problems into well-defined models and explain the tradeoffs to non-specialists Good communication and collaboration skills Internship or research experience in ride-hailing, delivery, logistics, or AV fleets (dispatch, routing, rebalancing, pricing) Algorithms deployed in production or used on real operational data Publications in operations research, transportation, or robotics venues